
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt


# Function to perform pairs trading strategy
def pairs_trading_strategy(df_pair, stockA, stockB):
    # Fit ARIMA model for residuals of stockA
    model_residualA = sm.tsa.ARIMA(df_pair[stockA], order=(1, 0, 1))
    result_residualA = model_residualA.fit()

    # Fit ARIMA model for residuals of stockB
    model_residualB = sm.tsa.ARIMA(df_pair[stockB], order=(1, 0, 1))
    result_residualB = model_residualB.fit()

    # Generate residuals for the selected stock pair
    residuals_advanced_A = result_residualA.resid
    residuals_advanced_B = result_residualB.resid

    # Calculate standard deviation of residuals for control limits
    std_residuals_advanced_A = (residuals_advanced_A - residuals_advanced_A.mean()) / residuals_advanced_A.std()
    std_residuals_advanced_B = (residuals_advanced_B - residuals_advanced_B.mean()) / residuals_advanced_B.std()

    # Generate trading signals based on standardized residuals
    threshold_advanced = 1.0
    signals_advanced = pd.Series(index=std_residuals_advanced_A.index, dtype=int)
    signals_advanced[std_residuals_advanced_A < -threshold_advanced] = 1  # Buy stockA
    signals_advanced[std_residuals_advanced_A > threshold_advanced] = -1  # Short stockA

    # Calculate standard deviation of standardized residuals for control limits
    std_residuals_advanced_A_std_dev = std_residuals_advanced_A.std()

    # Redefine control limits based on advanced model
    control_limit_upper_advanced = threshold_advanced * std_residuals_advanced_A_std_dev
    control_limit_lower_advanced = -threshold_advanced * std_residuals_advanced_A_std_dev

    # Generate revised trading signals based on control limits from advanced model
    revised_signals_advanced = pd.Series(index=std_residuals_advanced_A.index, dtype=int)
    revised_signals_advanced[std_residuals_advanced_A > control_limit_upper_advanced] = -1  # Short stockA
    revised_signals_advanced[std_residuals_advanced_A < control_limit_lower_advanced] = 1  # Buy stockA

    # Revised trading simulation based on advanced model
    capital_advanced = 100000  # Initial trading capital for advanced strategy
    position_advanced = 0  # 0 indicates no position for advanced strategy

    for i in range(1, len(df_pair)):
        if revised_signals_advanced.iloc[i] == 1 and position_advanced == 0:  # Buy signal and no position
            position_advanced = capital_advanced / df_pair[stockA].iloc[i]  # Invest all capital in stockA
            capital_advanced = 0
        elif revised_signals_advanced.iloc[i] == -1 and position_advanced > 0:  # Short signal and there is a long position
            capital_advanced = position_advanced * df_pair[stockB].iloc[i]  # Sell stockB and convert to capital
            position_advanced = 0

    # Calculate final capital for advanced strategy
    final_capital_advanced = capital_advanced + position_advanced * df_pair[stockA].iloc[-1]

    # Print the final capital for advanced strategy
    print(f"Final Capital for Advanced Strategy ({stockA}, {stockB}):", final_capital_advanced)

    # Visualize and compare results
    plt.figure(figsize=(12, 8))
    plt.plot(std_residuals_advanced_A, label='Standardized Residuals ' + stockA)
    plt.plot(std_residuals_advanced_B, label='Standardized Residuals ' + stockB)
    plt.plot(signals_advanced, 'o', label='Trading Signals (Advanced)')
    plt.plot(revised_signals_advanced, 'x', label='Revised Signals (Advanced)')
    plt.axhline(y=control_limit_upper_advanced, color='r', linestyle='--', label='Upper Control Limit (Advanced)')
    plt.axhline(y=control_limit_lower_advanced, color='g', linestyle='--', label='Lower Control Limit (Advanced)')
    plt.title(f'Residuals-Based Pairs Trading Strategy (Advanced) - {stockA} vs {stockB}')
    plt.legend()
    plt.show()

# Iterate over stock files from stock1.csv to stock24.csv
for i in range(1, 25):
    file_name = f'stock{i}.csv'
    
    # Load data
    df = pd.read_csv(file_name, parse_dates=['timestamp'])

    # Select a pair of highly correlated stocks for analysis
    stock_pairs = [("GARAN", "AKBNK"), ("VAKBN", "HALKB")]  # Add more pairs as needed

    # Iterate over stock pairs and apply pairs trading strategy
    for stockA, stockB in stock_pairs:
        df_pair = df[df['short_name'].isin([stockA, stockB])].pivot(index='timestamp', columns='short_name', values='price')
        df_pair = df_pair.pct_change().dropna()  # Calculate daily returns
        pairs_trading_strategy(df_pair, stockA, stockB)

/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

Final Capital for Advanced Strategy (GARAN, AKBNK): 39893.541353048306

/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

Final Capital for Advanced Strategy (VAKBN, HALKB): 19481.595871460093

/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

Final Capital for Advanced Strategy (GARAN, AKBNK): 0.0

/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

Final Capital for Advanced Strategy (VAKBN, HALKB): 0.0

/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
/Users/ahmetemregokalp/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'